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The two-factor model fits better for Chinese patients' understanding of illness causes than the original four-factor model.

Fine-tuned BERT models are better than LLMs for detecting bias in medical data.

The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.

Adding obesity data to machine learning models improves heart disease prediction accuracy.

The ideal haircut routine can be determined using a model based on hair growth and regular haircuts.

Light scatters differently from elliptical hair fibers than from circular ones, and a new model better predicts this behavior, especially for shiny highlights.

Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.

The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.

Tumor cell adhesion is linked to higher risk of SLN metastasis and melanoma recurrence, and a model including these factors predicts these outcomes better than one with just clinical data.

The model accurately predicts hair loss severity in alopecia areata.

The model accurately classifies hair conditions with 97% accuracy.

The model accurately simulates human hair growth and hair loss patterns.

Obesity and lifestyle strongly affect heart aging.

Combining genetic models helps improve heat tolerance in beef cattle.

The simulation showed that hypobaric pressure improves drug delivery through the skin, but stretching alone doesn't fully explain the increase.

Detecting early breast arterial calcifications can help assess cardiovascular disease risk.

The document suggests medicine should integrate biological and cultural factors and focus on holistic, equitable care.

AI can accurately diagnose hair and scalp conditions and suggest treatments.

Hair and wool strength is affected by the number and type of bonds in their protein structures, with hair having more protein aggregates than wool.

The Gas-Antisolvent process can be effectively modeled and optimized to create Finasteride, a hair growth drug.

Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.

Deep learning effectively diagnoses scalp disorders, but improvements are needed.

The new method provides more accurate vibrational frequencies for drug molecules than traditional models.

The research found that for a typical hair density, about 11 square centimeters of donor scalp is needed to get 800 hair grafts for transplantation.

Minoxidil does not work to inhibit lysyl hydroxylases in newborn mouse lungs.

AI can help diagnose Follicular Lymphoma by accurately identifying specific cell types.

Combining multiple algorithms predicts hair fall more accurately than using single algorithms.

The study concluded that hair growth in mice is regulated by a stable interaction between skin cell types, and disrupting this can cause hair loss.

A new method helps understand hair shine and various products improve hair care.

Skin can be cooled quickly and safely during laser treatments to protect it without affecting deeper layers.